Generating Junction Trees of Decomposable Graphs with the Christmas Tree Algorithm
نویسندگان
چکیده
The junction tree representation provides an attractive structural property for organizing a decomposable graph. In this study, we present a novel stochastic algorithm which we call the Christmas tree algorithm for building of junction trees sequentially by adding one node at a time to the underlying decomposable graph. The algorithm has two important theoretical properties. Firstly, every junction tree and hence every decomposable graph have positive probability of being generated. Secondly, the transition probability from one tree to another has a tractable expression. These two properties, along with the reversed version of the proposed algorithm are key ingredients in the construction of a sequential Monte Carlo sampling scheme for approximating distributions over decomposable graphs, see Olsson et al. [2016]. As an illustrating example, we specify a distribution over the space of junction trees and estimate of the number of decomposable graph through the normalizing constant.
منابع مشابه
Bayesian inference in probabilistic graphical models
This thesis consists of four papers studying structure learning and Bayesian inference in probabilistic graphical models for both undirected and directed acyclic graphs (DAGs). Paper A presents a novel algorithm, called the Christmas tree algorithm (CTA), that incrementally construct junction trees for decomposable graphs by adding one node at a time to the underlying graph. We prove that CTA w...
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